Method for generating visual mapping of knowledge information from parsing of text inputs for subjects and predicates

ABSTRACT

A method for performing relational analysis of parsed input is employed to create a visual map of knowledge information. A title, header or subject line for an input item of information is parsed into syntactical components of at least a subject component and any predicate component(s) relationally linked as topic and subtopics. A search of topics and subtopics is carried out for each parsed component. If a match is found, then the parsed component is taken as a chosen topic/subtopic label. If no match is found, then the parsed component is formatted as a new entry in the knowledge map. A translation function for translating topics and subtopics from an original language into one or more target languages is enabled by user request or indicated user preference for display on a generated visual map of knowledge information.

The present U.S. patent application is a continuation-in-part ofco-pending U.S. patent application Ser. No. 13/272,656 filed on Oct. 13,2011, which was a continuation-in-part of U.S. patent application Ser.No. 13/161,451 filed on Jun. 15, 2011.

TECHNICAL FIELD

This invention generally relates to visual mapping of knowledgeinformation, and particularly to performing relational analysis ofparsed input for visual mapping of knowledge information.

BACKGROUND

Knowledge created by human effort, research, and synthesis is constantlyincreasing. When people interact by communicating and exchanginginformation, the information shared between them will increaseexponentially. With the vast and ongoing explosion of information withinorganizations of people of every kind, the task of capturing and sharingknowledge within an organization from volumes of shared informationbecomes increasingly difficult.

The field of knowledge management has been developed to developframeworks for knowledge capture and sharing, and to employ strategiesfor managing knowledge processes within organizations. Explicitknowledge represents knowledge that is captured in a form that caneasily be communicated to others. The creation or synthesis of “newknowledge” is continually being added to “established knowledge”captured and shared within a group, organization, or community. Onestrategy to managing knowledge encourages individuals to explicitlyencode their knowledge into a shared knowledge repository, such as aknowledge database, so that they and others can retrieve knowledgeprovided to the repository. An important tool for encoding knowledgeinto and retrieving knowledge from a knowledge repository is knowledgemapping in which a visual representation (map) of knowledge objects in arepository is created so that users within a group, organization, orcommunity can visually assess the contents of the repository and accessdesired content readily and quickly.

One example of a knowledge mapping system is the MindManager™ visualinformation mapping software offered by MindJet, Inc., having worldwidecorporate headquarters located in San Francisco, Calif. The MindManager™software enables a user to create, add to, and use a knowledge mapcreated for a given theme or subject. A knowledge map is created andexpanded by entering labels for topics, subtopics, sub-subtopics, etc.,each of which represents a container (file, folder, or repositoryobject) for storing information content related to that label. Therelatedness of each topical label to one or more other topical labels isalso defined, resulting in a tree or network structure that can be addedto, expanded, modified, and re-organized in an intuitive manner usingvisual click-and-drop tools. Other trees or networks of knowledgecontainers can be linked and managed in a similar manner. The knowledgemapping is intended to bring visual order to ideas and information bydisplaying all related topical objects on a requested subject into asingle interactive view. A wide range of types of information content,attachments, notes, links, etc., can be stored in a container and viewedusing an integrated viewer. Knowledge content can then be visuallyaccessed, acted upon, and/or exported to other applications.

However, presently available knowledge mapping tools lack a convenientway to quickly or even automatically define a topic to be added and/orits relationship to other topics in a knowledge tree or network. Theyfurther lack a convenient way to quickly or automatically addinformation content to an already defined topic and/or its links toother information content in that or other topics in the knowledge treeor network.

SUMMARY OF INVENTION

In accordance with a preferred embodiment of the present invention, amethod for relational analysis of an input item of information having atitle, header or subject line and content to which it refers, saidmethod to be performed on a computer system operating a visual knowledgemapping software program for creating a visual map of input informationitems related to a given theme and to each other as topics and subtopicsin order to create a visual map of knowledge information of the giventheme, and said computer system having a storage repository for storinginformation content of topic and subtopics referenced on the visual mapof knowledge information, said method comprising:

Parsing a title, header, or subject line for an input item ofinformation into syntactical components of at least a subject componentand any predicate component syntactically related thereto;

Determining the subject component as a topic and any predicate componentas a subtopic relationally linked thereto;

Searching an index of any existing knowledge information map and anyexisting topics and subtopics created therein for a match to saidsubject component syntactically parsed from the input item ofinformation;

If a match to an existing topic is found, then formatting said subjectcomponent to be the same as the existing topic, and if no match isfound, then formatting said subject component as a new topic in theexisting knowledge information map, and

Storing any topic-related information content of the input item ofinformation in the storage repository of the computer system referencedto the formatted topic on the visual map of knowledge information,

Whereby input items of information can be quickly and conveniently addedto the knowledge information map created and maintained on the computersystem.

In the preferred embodiment, further steps of the method may be carriedout for any predicate component by:

Searching the index of the existing knowledge information map andexisting subtopics created therein for a match to said predicatecomponent syntactically parsed from the input item of information;

If a match to an existing subtopic is found, then formatting saidpredicate component to be the same as the existing subtopic, and if nomatch is found, then formatting said predicate component as a newsubtopic in the existing knowledge information map; and

Storing any subtopic-related information content of the input item ofinformation in the storage repository of the computer system referencedto the formatted subtopic on the visual map of knowledge information.

In a preferred modification to the method of the present invention, atranslation function of the computer system for translating topics andsubtopics from an original language into one or more target languages isenabled by user request or indicated user preference for display on agenerated visual map of knowledge information.

The parsed input can include both metadata and non-metadata entries. Ifmetadata is parsed, a recursion algorithm of the parsing subsystemreduces the associated non-metadata parsed input entry into a subjectcomponent and a predicate component, if a predicate component exists forthat entry. A metadata tag may override standard parsing rules andresult in either non-parsing or specific parsing of an entry.

When the parsing of input is completed, the matching subsystem acts onthe parsed input. If there is metadata instruction for matching, thatmetadata is executed first. For example, there might be metadataindicating that matching shall be bypassed and data directly stored withlinks specified in the metadata. If there is no metadata concerningmatching, the parsed input is then compared to stored data entries.Search algorithms are used to determine whether non-metadata inputshould be linked to other data entries, subsumed under other dataentries, rejected as duplicate data, established as original data, orprocessed in some other manner.

A particularly useful application for the method of the presentinvention is for creating knowledge maps of educational subjects forteaching. Knowledge maps for History, for example, can be formattedaccording to region (Asia, America, Europe, etc.), chronological order(ancient times to present day), thematically (women's movement,organized labor, occupations of conquered lands, etc.). From a “mastermap” for the History theme of instruction, a student learner can accessindividual knowledge maps, for example, for the history of philosophy,the history of art, the history of law, the history of science andtechnology, or the history of medicine.

Other objects, features, and advantages of the various embodiments ofthe present invention will be explained in the following detaileddescription with reference to the appended drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a flow diagram illustrating an overall architecture forrelational analysis of semantically parsed input for visual mapping ofknowledge information in accordance with a preferred embodiment of thepresent invention.

FIG. 2 illustrates an example of the Scan-to-Map process for scanning aninput item of information and formatting its semantic elements into anew knowledge map.

FIG. 3 illustrates searching a root directory of knowledge maps for amatch to a scanned subject of the input item of information.

FIG. 4 illustrates a close-up of the root directory of knowledge maps inFIG. 3.

FIG. 5 illustrates expansion of a knowledge map showing various aspectsof relational schema (“Why”, “How”, “So”, “Meaning”, “Analogy”, and“Concept”).

FIG. 6 illustrates another portion of the expanded knowledge map in FIG.5.

FIG. 7 illustrates a portion of an expanded knowledge map showing linksto multimedia websites, image galleries, videos, articles, etc.

FIG. 8 is a block diagram showing input channels to the Scan-to-Mapsystem.

FIG. 9 is a schematic diagram of a preferred embodiment of Scan-to-Mapsystem, in greater detail of which FIG. 9A shows the full logical flowpresent in the four underlying subsystems of the Scan-to-Map system,including the parsing subsystem shown in FIG. 9B, matching subsystemshown in FIG. 9C, formatting subsystem shown in FIG. 9D and storagesubsystem shown in FIG. 9E.

FIG. 10 is a schematic diagram of both traditional and non-traditionalsearch ontologies.

FIG. 11 shows an example of a storage database for the Scan-to-Mapsystem.

FIG. 12 is a schematic diagram of a Scan-to-Map visualization process.

FIG. 13 is a schematic diagram illustrating a preferred modification tothe method of the present invention in which subject and predicatelabels are translated to preferred target language(s).

DETAILED DESCRIPTION

In the following detailed description, certain preferred embodiments aredescribed as illustrations of the invention in a specific application,network, or computer environment in order to provide a thoroughunderstanding of the present invention. Those methods, procedures,components, or functions which are commonly known to persons of ordinaryskill in the field of the invention are not described in detail as notto unnecessarily obscure a concise description of the present invention.Certain specific embodiments or examples are given for purposes ofillustration only, and it will be recognized by one skilled in the artthat the present invention may be practiced in other analogousapplications or environments and/or with other analogous or equivalentvariations of the illustrative embodiments.

Some portions of the detailed description which follows are presented interms of procedures, steps, logic blocks, processing, and other symbolicrepresentations of operations on data bits within a computer memory.These descriptions and representations are the means used by thoseskilled in the data processing arts to most effectively convey thesubstance of their work to others skilled in the art. A procedure,computer executed step, logic block, process, etc., is here, andgenerally, conceived to be a self-consistent sequence of steps orinstructions leading to a desired result. The steps are those requiringphysical manipulations of physical quantities. Usually, though notnecessarily, these quantities take the form of electrical or magneticsignals capable of being stored, transferred, combined, compared, andotherwise manipulated in a computer system. It has proven convenient attimes, principally for reasons of common usage, to refer to thesesignals as bits, values, elements, symbols, characters, terms, numbers,or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the followingdiscussions, it is appreciated that throughout the present invention,discussions utilizing terms such as “processing” or “computing” or“translating” or “calculating” or “determining” or “displaying” or“recognizing” or the like, refer to the action and processes of acomputer system, or similar electronic computing device, thatmanipulates and transforms data represented as physical (electronic)quantities within the computer system's registers and memories intoother data similarly represented as physical quantities within thecomputer system memories or registers or other such information storage,transmission or display devices.

Aspects of the present invention, described below, are discussed interms of steps executed on a computer system, which may be one of anytype having suitable computing resources. Aspects of the presentinvention are also discussed with respect to an Internet systemincluding electronic devices and servers coupled together within theInternet platform, but it may be similarly implemented on any other typeof extended network system including wireless data or digital phonenetworks. Although a variety of different computer systems can be usedwith the present invention, an exemplary computer system is shown anddescribed in a preferred embodiment of the present invention system.

In the preferred embodiment, a relational analysis of new items ofinformation is performed to determine their relationships to existingtopics and/or items of information content already captured in a visualknowledge map. A commercially available knowledge mapping tool is usedto create and maintain a visual knowledge map. An example of a preferredknowledge mapping system for use in the present invention system is theMindManager™ visual information mapping software offered by MindJet,Inc., in San Francisco, Calif. (as described previously).

The present system is predicated on the premise that all knowledge is insome way interconnected. As such, it operates by performing a relationalanalysis of an input item of information to determine its topicaldefinition and relationships to existing items of information alreadymapped into a knowledge repository. In general, each input item ofinformation will have a title, header or subject line and informationcontent to which it refers. The relational analysis process is initiatedby executing a parsing of the title, header or subject line. The title,header or subject line is scanned and broken down into one or moresyntactical components of at least a subject and perhaps one or morepredicates. The syntactical components are then analyzed to determinetheir relationships to existing topics and subtopics already captured inthe knowledge map. If the input item is a new topic relevant to theknowledge map, then a new topical label is added to the knowledge mapand any topic-related information content is stored in the repositoryindexed by that topical label. If the input item is identified with anexisting topic already in the knowledge map, then it is formatted thesame as the matching topical label and any information content is addedto the repository referenced by that topical label. A similar processcan be carried out for formatting any predicate(s) syntactically parsedfrom the input item of information. The complete process of parsing ofinput, relational analysis of syntactical components, and addition toknowledge map is referred to herein as the “Scan-to-Map” process.

Referring to FIG. 1, a preferred embodiment for the Scan-to-Map methodin the present invention is illustrated showing its performing ofrelational analysis of parsed input items of information to a visual mapof knowledge information. The Scan-to-Map process 10 is configured as aninput front end to a Visual Knowledge Mapping System 20. The Scan-to-Mapprocess first scans the text of the title, header or subject line of aninput item of information. The text sentence or phrase is broken downinto subject(s), predicate(s), and other syntactical components. Thesyntactical functions of the parsed components are then determined.Their syntactical functions determine the type of object that it will beformatted as in the knowledge map. A “subject” syntactically parsed fromthe title sentence or phrase will be defined as a “parent” knowledgeobject, typically a topic on the knowledge map. One or more predicatesin the syntax of the title sentence or phrase will be defined as“offspring” knowledge objects, typically subtopics on the knowledge map.The formatting of “parent” and “offspring” objects is then carried intothe knowledge map.

In natural language processing systems, human languages are parsed byparser programs of varying levels of semantic depth and complexity.Human sentences are not easily parsed by computer programs, as there issubstantial ambiguity in the structure of human language. Some parsingsystems use lexical functional grammar, while others may use a morecomplex “head-driven phrase structure” grammar. Shallow parsing programsaim to find only the boundaries of major constituents such as nounphrases. Another popular strategy for avoiding linguistic complexity isdependency grammar parsing. Most modern parsers are at least partlystatistical, that is, they rely on a corpus of training data which hasalready been annotated (parsed manually). This approach allows thesystem to gather information about the frequency with which varioussemantic constructions occur in specific contexts. A widely usedapplication for semantic parsers of relatively low complexity is as aspelling and grammar checker for word processing programs. For example,commercially available, semantic parser programs for word processing inEnglish and other languages are offered by Babylon.com, based in SanFrancisco, Calif.

The parser generator deconstructs a sentence or phrase of scanned textand identifies its syntactical elements. Phrase-structure rules can beused to break down a natural language sentence or phrase intoconstituent parts. The parser generator program can associate executablecode with each of these grammatical rules, sometimes referred to as“semantic action routines”. These routines may construct a parse tree(or abstract syntax tree), or generate executable code directly. Theresulting elements in a parse tree can be converted to knowledge mapschema utilizing a markup tag (such as in XML tagging). The taggedelements can then be associated with analogous parent/offspringknowledge objects in the knowledge map. These knowledge objects are thenassociated with other objects in the knowledge map in a relationalformat that reflects their semantic levels of function. An example ofrelational links that may be used to reflect semantic level of functionare those corresponding to semantic functions of “Why”, “How”, “So”,“Meaning”, “Analogy”, and “Concept”. The relational links may bedetermined by relational rules automatically or ascertained throughhuman analysis to be incorporated manually.

For formatting in a knowledge map, the parsed objects may be associatedwith existing knowledge objects in a relational schema, using relationallinks labeled “Why” (to explicate a knowledge object's reasons); “How”(to explicate a knowledge object that represents a process); and “So”(to explicate a knowledge object's effect, result, or outcome). Theprogram determines whether a sentence element has such a relationship(as a “Why”, “How”, or “So” offspring) according to grammatical rules asto a) the order in which the sentence element appears; b) the structurein which it occurs; c) the type of meaning it expresses; d) the type ofaffixes it takes; e) Boolean predicates that the content must satisfy;f) data types governing the content of elements and attributes, and g)more specialized rules such as uniqueness and referential integrityconstraints.

The relational formatting process also uses relational links labeled“Meaning” (to explicate the significance of a knowledge object) and“Analogy” (to establish a relationship of a knowledge object to ananalogous knowledge object elsewhere—useful in comparing comparableevents in history). These relationships are ascertained through humananalysis, and are incorporated into the knowledge map manually. Newinput is mapped either by creating a new knowledge map for a subject orby incorporating it into an existing map.

The relational link “Concept” is used to establish a social, cultural,religious, political, economic, behavioral or other concept that relatesto a topic in the map to which it is appended, and to comparable topicsin other maps.

The program searches for the named subject in an index of existingknowledge objects. If a match is found for the named subject, theprogram determines what the various secondary predicates in the scannedtext are and correspondingly appends these secondary objects to thepre-existing map as offspring objects. If no match is found, the programwould regard the new input as a new subject, and create a new knowledgemap for that subject.

The parsed elements can be automatically formatted into an existingknowledge map by checking them against existing indices maintained in asystem, in the following order:

1. Root directory of knowledge maps, with links to listed subsidiarymaps.

2. Specific knowledge map with name of subject, with links to listedtopics

3. Specific topics with name of subject, with links to listed subtopics

4. Specific subtopics with name of subject, with links to listedsub-subtopics

5. Etc., to lowest level of relational hierarchy in specific knowledgemap

If no existing knowledge object is found in the system indices matchingthe named subject, the formatting procedure can automatically format thenamed subject by determining its relational link to an existing objectin the knowledge map. The automatic formatting can be performed byfinding the object at the highest level of relational hierarchy forwhich the named subject matches that object's semantic function of“Why”, “How”, “So”, “Meaning”, “Analogy”, and “Concept”, andincorporating the named subject as a subtopic to that object.Alternatively, the named subject can be manually defined as the labelfor a topic in the appropriate knowledge map, and its relational linksto existing objects can also be manually defined. In a similar manner,the named predicates and sub-predicates to the named subject can bequickly or even automatically defined relationally with respect toexisting subtopics of the determined topical object.

If the input item of information has a header of multiple sentences,such as an abstract for an article as information content, then acorpus-based parsing strategy may be used. For example, a “subject” maybe identified from a keyword appearing most frequently in the text, andany predicates can be identified from predicate syntactical componentsreferenced by the identified subject keyword.

While the primary purpose of the invention method is to enable theparsing, relational analysis, and formatting of input items ofinformation to be done automatically for speed, efficiency, andconvenience, the option for manual intervention of an expert in thesubject theme for knowledge map creation may also be included.

Conceptual or semantic mapping, which explicates the significance ofmeaning and relationship among knowledge objects, may be performedmanually, using the specialized expertise of a map creator in thesubject who comprehends the significance of various aspects of thatsubject and of their inter-relationships and relationships to othersubjects.

Using the Scan-to-Map process, input items of information can be quicklyor automatically added and properly formatted into a knowledge mapmaintained by the system. Input items of information from a wide rangeof sources can thus be conveniently added to a knowledge map, such aslinks to relevant webpages, email text and/or attachments, links fromannotated text, articles, blogs, etc. The Scan-to-Map process wouldenable such new items to be quickly or automatically added to aknowledge map simply by clicking and dropping the URL address, emailheader, or annotated link into the input field for the knowledge map.

FIG. 2 illustrates an example of the Scan-to-Map process for scanning aninput item of information and formatting its semantic elements into anew knowledge map. In this example, the title or subject line sentenceis scanned as text: “Hawaii has great scenery of mountains, beaches, andocean, people who are beautiful, intelligent, and hospitable, and foodthat is tasty, varied, and inexpensive.” Once this sentence is scanned,it is broken down into its subject, i.e., “Hawaii”, predicates, i.e.,“great scenery, people, food”, and corresponding sub-predicates, andrelationally formatted into the knowledge map as parent and offspring(and further offspring) nodes.

The formatting of predicates as subtopics can also be carried outautomatically. Consider the input sentence, “Hawaii has good weather,friendly people, and tasty food.” The system can parse the inputsentence in the Scan-to-Map function and identify a predicate (“goodweather”). A search of the topic indices reveals that under the subject“Hawaii” there is no subtopic “good weather.” The system will thenformat the predicate “good weather” to be a sub-topic of “Hawaii” andcreates an entry in the map for this new sub-topic/predicate, and linksthis entry to the parent “Hawaii” entry as an offspring object. All ofthe sub-predicates that relate to “good weather” (e.g., “blue skies”,“warm temperatures”, and “fluffy white clouds”) may similarly beconsidered sub-topics.

Every predicate becomes an identified syntactical object when a sentenceis broken down. Whether it is stored as content of a parent object orformatted separately as an offspring object depends on how much materialit presents. For example, when a topic presents more information thancan be viewed as an extension on the map or otherwise viewed withoutscrolling, the Scan-to-Map function can create new sub-topic offspringobjects that are linked to the parent object.

The Scan-to-Map program can determine whether to assign material to anexisting topic on a higher level—or set it up as a new map—by thecontext of the information scanned. For example, if a scanned article isdetermined (by the preponderance of subject words) to be concernedprimarily with the subject “Hawaii”, then a sub-topic “good weather” canbe assumed to relate to the parent heading “Hawaii” rather than toanother parent, say, “Florida.” If no topical match to “Hawaii” isfound, the subject can be considered to be a new parent object, distinctfrom all other parent subjects, and is placed in the index inalphabetical order of parent subjects.

FIG. 3 illustrates searching a root directory of knowledge maps for amatch to a scanned subject of the input item of information.

FIG. 4 illustrates a close-up of the root directory of knowledge maps inFIG. 3.

FIG. 5 illustrates expansion of a knowledge map showing various aspectsof relational schema (“Why”, “How”, “So”, “Meaning”, “Analogy”, and“Concept”).

FIG. 6 illustrates another portion of the expanded knowledge map in FIG.5.

FIG. 7 illustrates a portion of an expanded knowledge map showing linksto multimedia websites, image galleries, videos, articles, etc.

A particularly useful application for the method of the presentinvention is for creating knowledge maps of educational subjects forteaching. Knowledge maps for

History, for example, can be formatted according to region (Asia,America, Europe, etc.), chronological order (ancient times to presentday), thematically (women's movement, organized labor, occupations ofconquered lands, etc.). From a “master map” for the History theme ofinstruction, a student learner can access individual knowledge maps, forexample, for the history of philosophy, the history of art, the historyof law, the history of science and technology, or the history ofmedicine.

The knowledge-mapped instructional system can be used to deliver anentire curriculum of arts and sciences, law, and business courses, sothat the learner can acquire in this fashion nearly all subjects taughtin high school, college, graduate school, vocational school, corporateand government training programs, and elsewhere.

Once developed, the knowledge maps may be used in different ways. Theycan be used to analyze a subject on a stand-alone basis, or as a mediumfor instruction in a particular discipline. This can be done byaggregating and organizing a particular subject's maps in an orderedcourse of instruction. An online instructional environment in which thistakes place is a “course management system” that can include:

a) Lessons sections which contain respective knowledge maps forinstruction;

b) Discussion/analysis workspaces related to the knowledge maps;

c) Features sections such as: an Announcements forum; a Course Calendar;a Collaboration workspace with a Virtual Classroom and Live Chatfacility; a Course Roster, and more;

d) Mail section for email among users;

e) Course Information section that includes a course syllabus, systeminstructions, help files, and links to Technical Support;

f) Comprehensive Assessment Profile (CAP) to generate and maintainrecords of assessment scores and overall learner progress in a course,number of academic areas, or overall transcript.

Refinements to the instructional system can include the embedding orintegration of a map of a given subject (philosophy, for example) inanother, broader map (history, perhaps). In this case, the philosophymap can be highlighted in a distinctive color as it displays in thecontext of the broader history map that it is embedded in. This providesthe learner with a context that, in this case, explicates thedevelopment of philosophy as it occurs in the historical process. Thisenables the user to understand a subject from different perspectives,and to understand how the subject's knowledge objects relate toknowledge objects in other subjects.

The knowledge-mapped instructional system can incorporate extensivemultimedia into its formatting. Many of the knowledge objects in a mapmay be links to websites, bibliographies, articles and books, primarysources, films and videos, symphonies and other audio files, interactivemaps, museum exhibits, lectures, and more. They can also link todiscussion/analysis forums, chat rooms, assessment tools, and indexesthat are keyed to specific topics in the map.

The Comprehensive Assessment Profile (CAP) is an important part of theknowledge-mapped instructional system. The assessments are coordinatedwith the maps used for instruction and used to objectively measure thelearner's progress and provide a next-generation report card. Theassessments can be embedded in the knowledge maps at appropriate pointsin the learner's progression through the curriculum of maps, so as toprovide a measure of the learner's progress through specific aspects ofthe subject. The CAP can record percentile scores associated withassessment on specific aspects of a course. In this way, the CAP canprovide insight into specific areas of a learner's strength and weaknessin various topical areas and for an overall course. The CAP can providecomponent and aggregate scores, rather than assigning a traditionalletter grade to the overall course of instruction. This removes thesubjective factors in grading (such as teacher favoritism and studentpressure on the instructor) that result in grade inflation and loss ofcredibility of the credential.

A detailed description of one implementation example for the Scan-to-mapsystem will now be provided. FIG. 8 shows a flowchart of a process 800for the methods of providing input into the Scan-to-map system throughchannels that include the invocation of an API call 802, authenticationinto a secure form entry 804, launching of an independent application806, or any other supported access to the user interface 808 of theScan-to-map system.

In one aspect of the present invention, an independent process islaunched which connects to and invokes an API call to the Scan-to-mapsystem. Any agreed upon method of invocation is acceptable in theprocess, whether SOAP, WSDL, Web Method, Messaging subsystem, orotherwise. In a second aspect of the present invention, a secure entryform may be made available for the input of information into the system.In a third aspect of the present invention, an independent applicationmay be launched, such application which provides direct access to thesystem. A fourth aspect of the present invention supports allowingaccess through any type of supported user interface, which includes, butis not limited to, a browser, a mobile phone distributed application orany other similar type of user interface for which the system supportsaccess.

In a preferred embodiment, the input of information may also includemetadata that provides information for the system that may be used tomap new information properly into a new or existing knowledge map.

FIG. 9 is a schematic diagram of a preferred embodiment of a Scan-to-Mapsystem 900 for parsing text input and producing a connection between newinformation or the incorporation of existing information into aknowledge map. The initial parsing subsystem 902 includes a metadataanalysis coupled with a parsing subsystem for determining a title,header or subject line and the splitting of the input into a subjectcomponent and any predicate component syntactically related thereto.After the parsing and analysis of input, a matching subsystem 904 takesthe parsed input and determines whether there are any existing links towhich the input should be appended. A formatting subsystem 906 thenproduces both metadata and formatted data for inclusion within a new orexisting knowledge map. The formatted data is then stored by a storagesubsystem 908 for later retrieval.

In closer detail, the parsing subsystem 902 includes two centralcomponents. First, through every aspect detailed in process 800, thereis the possibility of the inclusion of both metadata and non-metadata.Metadata may include, but is not limited to, attributes within an XMLframework, XML nodes, or any type of data that is identified asmetadata. For example, in one embodiment of this subsystem, a form mayinclude supported fields which designate metadata, such as a checkboxthat indicates whether input text is metadata, or ActionScript thatincludes a visual tag that indicates metadata may be entered. Anyunderlying language is supported as long as such language provides avisual method of discerning how to add metadata. An API call 802, asdescribed in FIG. 8, may include specific calls that relate to metadata.

Once metadata is parsed, if the metadata is tied to any of a number ofset responses, it will determine how new non-metadata parsed input istreated. Non-metadata input can basically map to one of three options.The first option is for a complete linking of the non-metadata to anyeligible sets of existing non-metadata entries. The second option is forselective linking with specified limitations. The third option is forexclusion from linking altogether.

Second, the parsing subsystem includes all other data that is notmetadata. If information is not otherwise specified, it will be treatedby the parsing subsystem as non-metadata. Non-metadata is parsed using arecursive algorithm that begins with an entire entry. Entries aredetermined depending upon the method of input. For any method of input,there is a delimiter used to separate non-metadata from othernon-metadata or metadata. The recursion algorithm reduces an entry,commonly a title, header or subject line, into a subject component and apredicate component, if a predicate component exists for that entry.

The parsing algorithm may adopt multiple variations, as long as suchalgorithm is calculated to parse out an entry into its atomic parts, thesmallest subject and predicate parts available as part of the entry. Ametadata tag may override standard parsing rules and result in eithernon-parsing or specific parsing of an entry. Original text loaded as theresult of one of the supported methods in FIG. 8 is processed logicallyas shown in FIG. 9A, which shows the full logical flow present in thefour underlying steps of parsing in FIG. 9B, matching shown in FIG. 9C,formatting shown in FIG. 9D and storage shown in FIG. 9E.

Any text is checked first for metadata. If metadata is found, themetadata is read and both topics and subtopics created. If no metadatais found, then the text is checked for boundary markers. A boundarymarker is a standard grammatical element, such as a participial phrase.Without metadata, markers are simple and rely on a few grammaticalrules. The sentence “Rome was conquered” would be parsed as “Rome” and“was conquered” by the combination of the verb “was” and the use of “ed”in the subsequent word. In many cases, there will be no grammaticalrules applicable, in which case the entire text would be treated as atopic node as shown in 9B.

The matching subsystem 904 acts upon the parsed input. Once the parsingsubsystem 902 has completed parsing input, the matching subsystem actsin the following manner. In all aspects, parsed data may includemetadata with instructions for matching, metadata unrelated to matching,and non-metadata that has been parsed. For non-metadata that has beenparsed, if there is metadata instructions for matching, that metadata isexecuted first. For example, there might be metadata indicating thatmatching shall be bypassed and data directly stored with links specifiedin the metadata.

If there is no metadata concerning matching, the parsed input is thencompared to already stored data entries. Search algorithms will applymatching rules to determine whether non-metadata input should be linkedto other data entries, subsumed under other data, rejected as duplicatedata, established as original data, or processed in some other manner asspecified by the matching algorithm 904. FIG. 9C is a visual of thelogical flow of the matching subsystem. The parsing system makes aninitial division of text elements. If there is a match of a subjectcomponent at a Topic level, then it is subsumed under the existing topicand its predicate component may be added as a linked subtopic if nosubtopic match is found. A new subject component may be added to thedatabase as a Topic if its associated predicate component matches anexisting subTopic at a lower-level. If a match is found of a subjectcomponent to an existing subTopic, then the associated predicatecomponent may be added as linked subtext. If no metadata is present, adefault rule will exclude the addition of new links unless they matchstrictly.

The matching algorithm is based on an application of ontologicalprinciples derived from the method of Internet search pioneered by firmslike Google. In the originating phase of Internet search, firms likeYahoo! applied traditional ontological principles to the presentation ofweb site data. Much like the Library of Congress, Yahoo! attempted tomake categories that could help simplify the display of search results.As each book was added, the Library of Congress could only assign onedesignation to the book. A book, therefore, non-fiction and on theSoviet Union, would have a Dewey Decimal assignment based on “SovietUnion”. If there were substantial content related to the Ukraine, itwould not have a secondary assignment.

Yahoo! improved upon the Library of Congress taxonomy by providingadditional category possibilities for information. So a site on theSoviet Union, offering music for download and historical data, mightfall in the Country category, the Music category and the Historycategory. Yahoo! had the ability to add on new categories and ultimatelycould produce literally thousands of relationships upon which a usercould click to explore the World Wide Web.

Providing hard categories is inelastic, in that the resulting process isa series of links that are determined by a person or committee thatattempts in some way to enforce rules of structure on sites. But oftenin the search area users may behave in random ways. For example, in asearch for the history of the Soviet Union, potential search terms are“Soviet Union”, “Soviet history”, “USSR”, “USSR history”, “Russiancommunism history”, “Bolshevik history”, “Lenin Trotsky Stalintimeline”, “perestroika”, etc. For any of these searches, the target maybe common but the relative likelihood of a specific search may vary.

FIG. 10 is a schematic diagram of both traditional and non-traditionalsearch ontologies. Topics 1002 may involve a hard set of categories1004. Semantic links 1006 do not obey any type of structured hierarchy.There are relative probabilities, ranging from zero to one, of whether asemantic link can or should be formed between topics. In a standardsearch, the relative links are determined through a combination of metatags and internal calculations, returning certain results first,regardless of how common the search terms are used. As each new searchterm is used, and each link loaded, the result algorithm is altered toreindex the results for future retrieval.

In a preferred embodiment of the present invention, a comparisonalgorithm will run that factors in metadata first. The metadataoverrides, if present, any further comparison. If the metadata does notoverride the comparison, an algorithm will run that compares the topicand topic structure with all database topics. The comparison algorithm,after processing, will determine either that the topic is within one ormore hierarchies, in which case it will be linked to as many hierarchiesas there are matches, and statistics stored that indicate the linkvolume and the hierarchical locations. If there is no link, then a newtopic will be started. In this way, the hierarchies are freeform and cansupport as many hierarchies as there are potential topics, although inpractice there will be far fewer links either through metadatarestriction or comparison algorithm processing.

All objects within a parsed chain have as part of their initial parsinga designation gleaned from metadata or, in the absence of metadata,default behavior. The default behavior of any object is for it to becomea top-level item in its own right. Then the object will have one or morelevels representing its hierarchy. Each level is compared to thecontents of the database. For exact or high-probability matches, the newobject will inherit all of the original objects links in a recursivemanner. The system will rely in several instances on the metadatadelineating behavior of the matching algorithm.

Once the object is compared to the contents of the database, it is thenupdated and now if visually added will include relevant links to otherpotential chains. This comes, though, in a later step. The key is theflexibility of the system both automatically to match and also to allowmetadata to modify matching criteria. For example, there could be anobject with the topic of “Yugoslavia”, a subtopic “Art” and a predicate“Much art was destroyed during the independence movement of the early1990's”. Within the database, Yugoslavia would have links to alldescendant nations. Art would likely link to a high number of variationsand then the notes would not be linked elsewhere as they rely on thetopic and/or subtopic to provide appropriate context. Metadata in thismatch would include designations that would constrain future linking,thus reducing the odds of a bad link. Links may always be manuallyedited and/or removed, but that would then be a manual process, unlikethe automated process of creating links.

In the Appendix, a set of XML documents is shown illustrating fourexample mappings of XML from an original scan and resulting in a parsedand matched set of documents ready for database entry, with the databaseschema included.

Example 1 shows an initial parsing of the scanned in text of “AncientRome—aspects—frontiers and provinces—high\-water mark ofexpansion—Trajan breaks with Augustus\' policy of defensiveimperialism—conquests—Mesopotamia, Dacia (Romania), Sinai”. The originaltext is captured in all cases as a tag called “original”, as shown. Thenext step is the automatic generation of a tag “scanToMapFragment” withan attribute of “metaData” set to “yes” or “no”.

The examples of scans with metadata are shown in Examples 1 and 4, andthose without are shown in Examples 2 and 3. An original set of text mayhave delimiters, as shown in Examples 1 and 2, or not, as shown inExamples 3 and 4. In Example 1, the original text does have delimiters,in the example a hyphen.

All text where metadata is included is parsed into one of two nodes:“topic” and “subTopic”. A “topic” may have up to six differentattributes. The attributes determine whether and how the data will beinserted into the database tables. The topic in with the title “AncientRome” has the value “no” for “allowAsSubTopic”. This means that the textwill only be inserted into the Topics table and not in any other tablefor the original text. Further, the “literalLink” has a “no” value,which means that the database query will first look for similar topics,such as “Old Rome”, “Early Rome”, etc. A decision tree provides adetailed breakdown of process and steps based on different values forthe various potential nodes, assuming that metadata is present.

First, the complete text is divided into the first element andsubsequent elements. The first element is defined as the first string.The first element is added to a “topic” node. All other elements areadded to a “subtopic” node. Then the first element is checked both foralphabetic case as well as whether to link literally to other values. Inthe strictest case, the term “ancient Rome”, for example, would notmatch “Ancient Rome” or “Old Rome”. If there is a match, then the firstreturned result is provided as the “topic” ID from the database.Otherwise, a new “topic” ID is added into the database and its ID isreturned.

Next, all subsequent strings are checked for Proper Nouns (uppercase).If the metadata allows for partial topic or partial subtopic extraction,Proper Nouns are then added as their own “topic” node with the sameprocess of determining whether they are new or an update. If themetadata supports allowing a “topic” or “subtopic” as “subtext”, thennew “subtext” nodes are created. For any “topics” now present, if theyallow “subtopics”, then “subtopic” nodes are also created.

FIG. 11 illustrates components of a Scan-To-Map Database. As shown,there are eight core tables in the Database that are updated as input isprocessed. Four are tables for storing pure node data, such as “Topics”1112, “SubTopics” 1114, “SubTexts” 1116, and “Originals” 1118. Theoriginal text is stored to allow for future processing or manualintervention in certain cases. There are also four linking tables“TopicsSubTopics” 1122, “SubTopicsSubTopics” 1124, “TopicsSubTexts”1126, and “SubTopicsSubTexts” 1128. The linking tables supportmany-to-many relationships among different node types. “SubTopics” maylink to “Topics”, “SubText”, and also to themselves. Several “SubTopics”can be chained together. “SubText” is the final node in many chains, andtherefore cannot be linked to other “SubTexts”. “Topics” are the startof a chain and therefore may not link to other “Topics”. The metadatachoices determine whether to allow new and different combinations ofinformation to other information.

Once the nodes and choices have been generated, then the various tablesare updated. In the example, there would be several new entries in thelinking tables so that if a user types in “Ancient Rome”, there would beat least one set of results that, if clicked on, would eventually leadto the full chain as shown in the original text. In the Appendix,Example 1 demonstrates the effective use of metadata. Example 2generates a scan-to-map where only non-metadata are provided. In thatcase, default business rules determine parsing. Instead of multiple“Topic” and “subTopic” nodes, there are a number of “title” nodes thatcarry the parsed text from the original string. Once the text fits astandard rule around grammar, which is that prepositions indicate aphrase, then “subText” nodes are added. Then, all capitalized stringsare added as “Topic” nodes. Finally, the database is updated with allrelevant items inserted or used to update any table matching their nodedescription, and the linking chain is then added through linking tablesso that if “Ancient Rome” is clicked on, it opens up the remainingelements of the chain.

An original string with no delimiters can still have metadata. In thatcase, the string is treated as though it was the sole string in thedocument and then broken down by metadata rules. For example, since the“Topic” node allows for “allowAsSubText”, the string “Trajan disagreeswith Augustus” would be added to the SubText table. Otherwise, theprocess is the same as in the metadata example.

An example of no delimiters and no metadata would be the most difficultto analyze. In this case, the text would be treated as a single string.In this case, default parsing rules would result in the XML shown. Inother words, the “title” and “subText” nodes only appear in exampleslacking metadata. The database is updated based on the combination ofattributes and nodes generated from the text, and the text is stored forlater review. As the database builds, the various potential combinationsare increased. So, for example, if “Ancient Rome” is entered in twoexamples, then both chains become available for viewing by loading“Ancient Rome”.

This is an example set of metadata that provides purely parsing support.The metadata may be programmatically enhanced to provide further refinedprocessing capability. Once matching is complete, the formattingsubsystem 906 is used to further prepare input for storage or otherprocessing. In operation, as matched data becomes ready, the formattingsubsystem then processes all metadata applicable to matching coupledwith all matched non-metadata. Metadata that applied to formatting mayinclude instructions related to format. For example, a metadata tag mayindicate that a subject line of “Hawaii” is applicable to a specificcontext only, such as “Pearl Harbor”, and orphaned from other links. Inthis sense, in one aspect the metadata may include an attribute labeled“limited_context=Pearl Harbor” or some other metadata that pertains tohow the data will be handled in any subsequent matching operations.

FIG. 9D illustrates a logical mapping of the process for formatting. Themetadata tag “caseSensitive” determines whether only XML that isformatted identically will be identified as a topic or subTopic.Capitalized strings determine whether or not the format of the stringwill allow it to be used as either a topic or subTopic. Topics,subtopics and subtext all are capable of updating or creating a newregion on the Scan-to-map system. The earlier matching process isdeterminative of most of the metadata that will be applied toformatting, with these noted exceptions. In a preferred embodiment, itis possible to define new rules that are driven by metadata. For now,the key is that the metadata is supported so that all parsing begins byseeking out and identifying all metadata contained as attributes withintags.

In the formatting process, if there is a set of matched data, that datawill acquire any metadata and other data necessary to conform it toexisting matched data. Formatting also includes placing data into thestatus of a subtopic. Formatting is the mechanism by which a map iscreated and/or updated. If matched data is a subtopic of some otherdata, then formatting will result in the matched data being tagged forinclusion under the existing topic.

In all aspects of the process, any matched data will require theaddition of metadata that identifies one or more knowledge maps to whichthe matched data will be linked. If the matched data has no link, itwill become its own knowledge map and will be formatted as such.

Once formatted, data then will be included in the storage subsystem 908by a data processing algorithm. There are multiple database products,each of which include access methods supported by a plethora ofprogramming languages and application-specific connectors. Formatteddata is then stored pursuant to a storage algorithm that may includestorage metadata. If metadata relates to the manner in which formatteddata will be stored, this may alter how formatted data is in factstored. For example, it could be that historical data should be storedand indexed in a manner that allows for rapid retrieval. Metadata couldbe provided that indicates that the formatted data should be indexed andcross-referenced for rapid later access. It might even include keywordsthat are added to separate tables within a database. FIG. 9E provides anoverview of the two steps to a database commit. First, any queries aregenerated by the earlier steps shown in FIGS. 9A, 9B, 9C and 9D. Thenthose queries are run and all data is committed to the database shown inFIG. 9E for future retrieval and processing.

FIG. 12 is a schematic diagram of the Scan-to-Map visualization process1200. Within the visualization process are a series of visual topics1202, methods 1204 of either opening or closing links to sub-topics andother materials, and a visual method 1206 of adding or updatingmetadata.

In any aspect of the present invention, the visual topic 1202 ispresented through whatever supported codebase is used to display visualdata, ranging from a compiled language such as C# and Java, or aninterpreted language such as PHP or JavaScript. The visual topic may bean original topic or a linked subtopic and acts as a root element of thevisualization of the knowledge map. The visual topic, as the baseelement, can be combined with multiple additional visual topics to forma map or a chain of topics and subtopics within a related chain.

Any visual topic may have an opening/closing symbol 1204. The purpose ofthe opening/closing symbol is to provide a rapid method of eitherrevealing or concealing other visual content linked to the presentvisual topic. The symbol can be of any type, such as a visual plus andminus sign, some graphic, or any other visual method of revealing theability to view or hide links among visual topics.

In a preferred embodiment, the metadata subsystem 1206 also has sometype of visual element that can be used to hide or show options to addor update metadata. Within the metadata subsystem, many options arepossible.

It is possible to allow metadata referring to any of the subsystems ofFIG. 9. For example, one may choose to add a parsing reference that avisual topic is a subject reference that includes a predicate and shouldtherefore not be parsed at all. Another example would be updatingmetadata to indicate that the present visual topic addresses an aspectsuch as “Why?” and can only be included in a knowledge map related toaddressing the “Why?” of a topic or subtopic.

There are key technical advantages to the preferred embodiment of thepresent invention. First, through the use of automated subsystems, thereare ways to automatically add and update relationships among topicswithin a knowledge map. Second, the visual system is designed for speedand ease-of-use. Speed and ease-of-use is an advantage of the systemthat will also provide an easier and more systematic search and matchingresult.

At present time, XML, or extensible markup language, is the most commonmethod of providing a high-level ontology for data. Through theemployment and development of XML as a language of exchange, the textbecomes simple to interpret, as opposed to machine-language where suchlanguage is difficult for even the most well-trained professional tocomprehend.

XML is limited in some senses as it would be slower than other methodsfor actually delivering text to the visual display. Text is very slow inretrieval because it takes time to parse and is often difficult to indexeffectively. Other schema may provide similar profit for the presentinvention and are welcome if they technically assist in the goal ofquick topic definition or automatic topic processing.

The system is highly expandable. Modules may be added and othermodifications made to leverage the ability to deliver knowledge maps.The preferred embodiment is compatible to some degree with knowledgemapping systems like MindManager, but is distinct and unique, asdescribed herein.

The relations identified by the system include why, how, so, meaning andanalogy. There is room for additional relations, too, as the taxonomy ofrelations is flexible. The system is flexible and adaptable.

The technical subsystems are amenable to a wide range of code languages.There are minimal restrictions, save that the user interface must bepresentable in a web-browser in one aspect of the present invention.Also, as mobile technologies proliferate, adaptability toyet-to-be-developed technologies is understood. If a newer language ormethod of presenting information, such as virtual reality, develops intoa solution that can provide support for the system, then such newtechnology is explicitly assumed to be compatible with thespecifications of the present invention.

Each knowledge object is declared as a result of the use of the parsingsubsystem 902 coupled with the interpretation of any applicablemetadata. A knowledge object is extensible, and may or may not include asubject and a predicate, simply a subject, or any other arrangement ofdata as necessary to present the knowledge map.

Objects in the knowledge management world may consist of discrete dataelements, such as a city name, or an array of elements, such as the 50states or a token that represents an element, such as IA for Iowa, or 1for Alaska. An object may be programmatically extendible or extendiblethrough how it is linked with other objects. A root object has no objectto link to that is closer to the root-level than the object.

An object can include an array that also includes sub-arrays. Forexample, an object might include three arrays, one with a sub-array ofmetadata settings, one with a sub-array of links and one with asub-array of position information related to its visual location.

In object-oriented programming, there are different conceptualizationsof objects. Strict object use in a compiled, strongly-typed programminglanguage has a different denotation than is used here. The objectconcept referenced herein is more in line with the object as defined byboth JavaScript and ActionScript, that is two languages which havetop-level objects that are extensible in any manner by any type as partof their hierarchy.

Within the formatting subsystem, there is no specific requirement as tohow elements must be linked. In one embodiment of the present invention,the links would work first with a root directory of knowledge maps,linked to subsidiary maps, further linked to listed topics within themaps, linked to subtopics, linked to sub-subtopics until the lowestlevel of relational hierarchy is reached. Any topic might be on multiplelevels of different hierarchies. In fact, it is entirely possible that ahierarchy could be recursive or self-referencing. For example, a warcould have a country that first aligned to one set of countries and thento another. So the country would need to reference both one side andthen another in a conflict. If a user clicks to expand a link, theywould need to reference two different sets of links. This could beaccomplished through a recursive process or a remapping process thattakes into account the original topic and sets either a default value orsome other similar system of linking up information.

Information is often ambiguous and requires selecting manually among arange of options. Through the use of a flexible, adaptable option model,this goal may be attained programmatically using a wide range oflanguages and delivery systems targeting any type of computing device.Currently, devices range from mobile to desktop to laptop to server andmay types of hybrids. The present invention does not rely on anyspecific platform for usage or distribution and is amenable to new typesof technology, as long as they comply with the requirements outlined inFIG. 8.

FIG. 13 is a schematic diagram illustrating a preferred modification tothe method of the present invention in which a translation function 100is used to translate subject and predicate labels are translated topreferred target language(s). First at input 102, a user elects to viewinformation anywhere in the system, whether to interact with the systemor to view user-generated content. The system then retrieves relevantinformation through the process of applying principles of relationalontology to the desired information input 104. In this process, standardtranslation algorithms are used and the information processing focuseson the nature of the information as opposed to the language of theinformation.

The presentation (map generation and display) system 200 determineswhether additional target language(s) for display on the knowledge mapsare being requested. There are several ways in which this might bederived. For instance, if the source material itself originated in aforeign language or if the user has demonstrated the usage of foreignlanguages in the past or if the user has selected one or more foreignlanguages as a preference for system usage. In other words, thetranslation function for other target languages is invoked if relevantto the search.

The system utilizes the most efficient algorithm available to translatethe retrieved information 104 into one or more target languages. Thetranslations themselves are stored in the system and associated to thesought content through a system of rules that apply to the sourcecontent as well as the translations. The translations may themselves beanalyzed and broken down into fragments or subsets which are also storedfor future retrieval. The Subject Field and other Predicate labels areused to assist the machine translation function with contextual orrelatedness information to help resolve a semantically correcttranslation from among various homologous options.

For example, a user uploads text in French that includes the phrase“Vive la France!” The translation into English would be “Long liveFrance!”. For a user with no language preference, the original text“Vive la France!” would appear, but the system would automatically notethe English preference and provide the translation as an alternate. Thesystem utilizes an algorithm in this case which is provided by thestored language of the original text (French) coupled with the userpreference (English), thus resulting in automated translation. Usersmight also denote that the preferred translation is “France forever”and, if enough users preferred this translation, it would be stored inits own right as the preferred translation. Appendix 2 provides anexample of text entered in English and then translated.

The system first starts with the largest available contextual set ofinformation. If a phrase is entered, the phrase is translated. If a pageis entered, the content of the page is translated. Then the smallestdiscernible syntactic unit is derived and translated. Through gradualusage, the syntactic units are refined and all include associatedcounts.

Next, the system displays the sought information as an update to users106 while the process of translation is taking place. Finally, thepresentation system 200 displays both the sought information and thesought information in the relevant target language or target languages.The presentation system has the capability of adapting to multi-lingualusage in various contexts through the process of analysis. For example,a user searching on information about Don Quixote might also retrieveresults in Spanish given that it is the language of the original text.This is as opposed to retrieval due to a stated preference. Also, if amajority of users happen to view the original text, it might also appearon this basis.

The user may tap, gesture, click or utilize any acceptable system actionto replace text or augment text with translation material manually. Thesystem would learn through preferences when to supply such informationthrough an automated fashion, but would support any existing usagepattern.

As with the Scan-to-Map function, the subsystem relies on an automated,gradual process to increase accuracy and relevancy within translation.For example, if most users search upon or create content with the word“old”—“vieux” (French), “alt” (German) as opposed to “ancient”translated to “ancienne” (French) or “antiken” (German), then the systemtranslation of “old” will likely result in “vieux” as the first choiceand “ancienne” as the second, even though conventional translationpreference order would be reversed.

An example of a server or PC-based machine translation system inmultiple languages is commercially offered by SYSTRAN Translation, SanDiego, Calif. The SYSTRAN machine translation engine combines thestrengths of rule-based and statistical machine translation. An exampleof a Web-based machine translation system in multiple languages isoffered by Babylon Ltd., Israel.

All translations are ultimately the production of an algorithm ofapproximation. The translation processing system using contextual orrelatedness information of defined topics and subtopic labels in theknowledge mapping system allows for the leveraging of existingontological paradigms to return an ultimately far more accuratetranslation for the user base.

Many other modifications and variations may of course be devised giventhe above description of the principles of the invention. It is intendedthat all such modifications and variations be considered as within thespirit and scope of this invention, as defined in the following claims.

The invention claimed is:
 1. A method for relational analysis of inputitems of information, each having a title, header or subject line andcontent to which it refers, said method to be performed on a computersystem operable with a visual mapping software program for creating avisual map of input items of knowledge information related to a giventheme and to each other as topics and subtopics in order to create avisual map of knowledge information of the given theme, said computersystem including a storage repository for storing information contentrelated to the given theme for topic and subtopics referenced on thevisual map of knowledge information, said method comprising: a) parsinga title, header, or subject line for an input item of information intosyntactical components of at least a subject component and any predicatecomponent syntactically related thereto; b) determining the subjectcomponent as a topic and any predicate component as a subtopicrelationally linked thereto; c) searching a topic-subtopic index of theknowledge information map for any existing topics or subtopics createdtherein for a match to said subject component syntactically parsed fromthe input item of information; d) if a match to an existing topic orsubtopic is found, then formatting said subject component to bedisplayed the same as the existing topic or subtopic, and if no match isfound, then formatting said subject component to be displayed as a newtopic in the existing knowledge information map, and also formattingsaid predicate component to be displayed as a subtopic of the displayedtopic, wherein in said searching for match step, if there is a match ofa subject component at a topic level, then it is subsumed under theexisting topic and its predicate component is added as a linked subtopicif no subtopic match is found, and a new subject component is added as atopic if its associated predicate component matches an existingsubtopic, and wherein a translation function of the computer system fortranslating topics and subtopics from an original language into one ormore target languages is enabled by user request or indicated userpreference for display on a generated visual map of knowledgeinformation.
 2. A method according to claim 1, wherein the translationfunction uses contextual or relatedness information indicated by thetopics and subtopics in their original language to help resolvesemantically correct translation into the target language(s).
 3. Amethod according to claim 1, further comprising: a) searching the indexof the existing knowledge information map and existing subtopics createdtherein for a match to said predicate component syntactically parsedfrom the input item of information; and b) if a match to an existingsubtopic is found, then formatting said predicate component to be thesame as the existing subtopic, and if no match is found, then formattingsaid predicate component as a new subtopic in the existing knowledgeinformation map.
 4. A method according to claim 1, further comprisingstoring topic-related information content of the input item ofinformation in the storage repository of the computer system referencedto its formatted topic on the visual map of knowledge information.
 5. Amethod according to claim 1, wherein the parsed input components arechecked against a hierarchy of indices in the following order: a) rootdirectory of knowledge maps, with links to listed subsidiary maps; b)specific knowledge map with name of subject, with links to listedtopics; c) specific topics with name of subject, with links to listedsubtopics; and d) specific subtopics with name of subject, with links tolisted sub-subtopics.
 6. A method according to claim 1, wherein aknowledge map is created and maintained for an educational subject forteaching on a stand-alone computer connected to an online network as amedium for online instruction in the educational subject, wherein aplurality of knowledge maps are aggregated and organized in an orderedcourse of instruction, and wherein the ordered course of instruction ismanaged by a course management system.
 7. A method according to claim 6,wherein the course management system includes one or more functionalcomponents from the group consisting of: a) lesson section which containrespective knowledge maps for instruction; b) discussion and analysisworkspace related to the knowledge maps; c) an Announcements forum; d) aCourse Calendar; e) a Collaboration workspace; f) a Course Roster; g) amail section for communication among users; and h) course informationsection.
 8. A method according to claim 6, wherein the course managementsystem includes a Comprehensive Assessment Profile (CAP) section togenerate and maintain records of assessment scores and overall learnerprogress.
 9. A method according to claim 6, wherein the coursemanagement system includes multimedia knowledge objects with links toinformation content such as websites, bibliographies, articles andbooks, primary sources, films and videos, symphonies and other audiofiles, interactive maps, museum exhibits, and online lectures.
 10. Amethod according to claim 8, wherein the Comprehensive AssessmentProfile (CAP) section includes assessments that are coordinated withknowledge maps used for instruction and used to objectively measure alearner's progress via an online report card.
 11. A method according toclaim 1, wherein said parsing of a title, header, or subject line for aninput item of information includes deconstructing a sentence or phraseof scanned text and identifying its syntactical elements usingphrase-structure rules to break down a natural language sentence orphrase into constituent elements, and tagging the constituent elementsas corresponding knowledge map schema to be displayed as analogousparent/offspring knowledge objects in the knowledge map.
 12. A methodaccording to claim 11, wherein the knowledge map schema include thosecorresponding to a semantic function of the group consisting of: “Why”,“How”, “So”, “Meaning”, “Analogy”, and “Concept”.
 13. A method accordingto claim 12, wherein the semantic function of a sentence element isdetermined according to one or more grammatical rules of the groupconsisting of: (a) the order in which the sentence element appears; (b)the structure in which it occurs; (c) the type of meaning it expresses;(d) the type of affixes it takes; (e) Boolean predicates that thecontent must satisfy; (f) data types governing the content of elementsand attributes, and (g) more specialized rules such as uniqueness andreferential integrity constraints.
 14. A method according to claim 1,wherein the parsed input includes metadata associated with anon-metadata entry, and the metadata are parsed as an instruction tooverride parsing rules and determine subject and predicate componentsfor the associated non-metadata entry.